Recommendation Engine Technology Powering Crossing Minds

Our Deep Collaborative Filtering approach scales to settings surpassing traditional boundaries with hidden layers and non-linear activation functions. By adding hidden layers and training with in-house proprietary sparse gradient descent, our AI can identify and learn more complicated non-linear patterns between users and items.

Semantic Graphing makes the goal of finding correlations between seemingly unrelated data simpler by using variables such as metadata, labels, tags, genre, actors and more. By doing this we can make sense of semantic data in the same way a human mind would. In a nutshell, if we know a user likes a certain item and that item shares similarities with another, we know the second item will make a good recommendation, too.

Deep Content Extraction allows Crossing Minds models to recommend items no one has interacted with yet. The algorithms can understand the content’s genre by automatically extracting information from items such as cover art, a synopsis, or reviews. Deep Content Extraction allows Crossing Minds models to generate accurate recommendations for new items as soon as they are available in the data set.